To address such challenges as an uncertain number of demagnetization poles of the permanent magnet synchronous motor (PMSM) and cases in which the fault cannot be located, this paper proposes a fault identification and location methodology based on the analysis of the motor stator current. First, the influence of the irreversible demagnetization of permanent magnets on the analytical model of the back electromotive force (Back-EMF) of the rotor in a single motor stator slot is analyzed. Moreover, considering the topology of the motor, the influence of the demagnetization fault on the stator phase current and branch current is analyzed. Since the stator phase currents cannot diagnose the partial demagnetization faults of PMSM with some topological structures, the stator branch current is selected as the signal for the identification and localization of the demagnetization fault. Secondly, the demagnetization fault diagnosis and mode recognition of the motor are carried out through the amplitude of the real-time branch current and the harmonic components of the PMSM. A sample database of demagnetization faults is established through calculation and normalization of the residual value of the stator branch current and the branch current of the healthy motor after demagnetization in one pole order. The fault threshold is obtained by analyzing the residual of the branch current of uniform demagnetization and the Pearson correlation coefficient of the fault sample database. Then, the correlation coefficient between the real-time branch current residual value of PMSM and the fault sample database is analyzed, and the number of demagnetization poles and the fault location are determined by the number and location of the calculated correlation coefficient exceeding the threshold. Finally, the feasibility and effectiveness of the proposed method are verified by the finite element analysis (FEA) results.

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and hybrid fault of a permanent magnet synchronous motor (PMSM) with a slot pole ratio of 3/2 and several times of it, this paper proposes a method to identify the rotor fault based on the combination of branch current analysis and a machine learning algorithm. First, the analysis of the electrical signal of the PMSM after various types of rotor faults shows that there are differences in the time domain of the electrical signal of the PMSM after three types of rotor faults. Considering the symmetry of the structure of the PMSM with a slot-pole ratio of 3/2 and its integer multiples, the changes in the time domain of the phase currents cancel each other after the fault, and the time domain fluctuations of the stator branch currents that do not cancel each other are chosen as the characteristics of the fault classification in this paper. Secondly, after signal preprocessing, feature factors are extracted and several fault feature factors with large differences are selected to construct feature vectors. Finally, a genetic algorithm is used to optimize the parameters of a support vector machine (SVM), and the GA-SVM model is constructed as a classifier for multifault classification of permanent magnet synchronous motors to classify these three types of faults. The fault classification results show that the proposed method using branch current signals combined with GA-SVM can effectively diagnose faulty PMSM.

To solve the optimization issues of interior permanent magnet synchronous motors (IPMSMs) and ensure a large output torque while minimizing torque ripple and core loss, the multi-objective optimization strategy should be employed. In this study, we took an 8-pole, 48-slot IPMSM as a specimen. First, the width and thickness of the permanent magnet (PM) and the rotor bridge structures were pre-selected as optimization parameters, while torque ripple and core loss were taken as optimization targets. Then, the Taguchi method to perform orthogonal experiments was employed to select the multi-parameter combinations that make the experimental results stable and with little fluctuation. To ensure the optimal results, the function equations were obtained by multivariate nonlinear fitting, while the parameters were optimized by particle swarm optimization (PSO). Finally, the optimal results were verified by the Finite Element Method (FEM). The results show that our proposed hybrid method can provide an optimal design strategy with better performance such as smaller torque ripple and core loss while maintaining a larger output torque.

A hard issue in the field of microrobots is path planning in complicated situations with dense obstacle distribution. Although the Dynamic Window Approach (DWA) is a good obstacle avoidance planning algorithm, it struggles to adapt to complex situations and has a low success rate when planning in densely populated obstacle locations. This paper suggests a multi-module enhanced DWA (MEDWA) obstacle avoidance planning algorithm to address the aforementioned issues. An obstacle-dense area judgment approach is initially presented by combining Mahalanobis distance, Frobenius norm, and covariance matrix on the basis of a multi-obstacle coverage model. Second, MEDWA is a hybrid of enhanced DWA (EDWA) algorithms in non-dense areas with a class of two-dimensional analytic vector field methods developed in dense areas. The vector field methods are used instead of the DWA algorithms with poor planning performance in dense areas, which greatly improves the passing ability of microrobots over dense obstacles. The core of EDWA is to extend the new navigation function by modifying the original evaluation function and dynamically adjusting the weights of the trajectory evaluation function in different modules using the improved immune algorithm (IIA), thus improving the adaptability of the algorithm to different scenarios and achieving trajectory optimization. Finally, two scenarios with different obstacle-dense area locations were constructed to test the proposed method 1000 times, and the performance of the algorithm was verified in terms of step number, trajectory length, heading angle deviation, and path deviation. The findings indicate that the method has a smaller planning deviation and that the length of the trajectory and the number of steps can both be reduced by about 15%. This improves the ability of the microrobot to pass through obstacle-dense areas while successfully preventing the phenomenon of microrobots going around or even colliding with obstacles outside of dense areas.

A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent model of the magnetorheological damper are identified through experiments. Secondly, a simulation model of the whole vehicle multi-degree-of-freedom vehicle dynamics including magnetorheological damper is established, and the whole-vehicle Linear Quadratic Regulator (LQR) controller is designed. Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. Simulation results show that the proposed control method is able to improve the body roll angle, body pitch angle, and suspension dynamic deflection well on the basis of ensuring no deterioration in other performance indexes, ensuring good attitude control capability of the vehicle and verifying the feasibility of the control strategy.

Fault-tolerant control (FTC) is crucial for enhancing the safety, reliability, and tracking performance of steer-by-wire (SBW) systems. This paper focuses on actuator effectiveness reduction faults and establishes an analytical model of the SBW system that incorporates motor disturbances, steering feedback, and self-aligning torque characteristics. To solve the inadequate tracking accuracy resulting from actuator fault and system disturbances, an adaptive sliding mode fault-tolerant control strategy based on a variable-speed reaching law (VSRL-ASMFTC) is proposed. By integrating an adjustment function into the constant-velocity reaching polynomial and designing an adaptive law, dynamic updating of the control law is achieved. Furthermore, the closed-loop stability of the system is proven based on Lyapunov’s stability criterion. The example information demonstrates that the proposed method reduces the root mean square (RMS) of tracking error by nearly 40% in three typical conditions compared to adaptive fault-tolerant control methods (ASMFTC), and the disturbances of the controller are relatively slighter. This indicates that the VSRL-ASMFTC mitigates system chattering, optimizes the system’s anti-disturbance capability and robust stability, and improves fault tolerance efficiency in the presence of actuator failures. It maintains good tracking performance of the system, which provides a basis for the design of high-performance fault-tolerant control strategies.